Computer Engineering Commons^™

A Censorship Resistant News Website Using The Ethereum Blockchain, Hoang Lai

Master's Projects

Misleading information, false claims, and fabricated news articles not only misguide readers but also undermine the trustworthiness of the news platforms themselves. The blockchain provides decentralized, immutable data storage and offers a promising solution to prevent censorship on news websites. Compared to traditional news websites, a decentralized application (dApp) offers benefits such as greater stability and resistance to information manipulation. A decentralized web app is harder to attack than centralized servers since the database is stored across a blockchain network. Moreover, blockchain prevents censorship by letting readers check data across all blocks in the Blockchain, which is good for a …

Predicting Remaining Useful Life Of Turbofan Engines On Cmapss And N-Cmapss Using Deep Recurrent Neural Networks, Samaikya Tippareddy

Master's Projects

Aircraft engines are susceptible to failure at multiple points over their lifespan and need replacement or repairs. The ability to proactively determine how long an engine will function helps avoid fatalities and build a reliable prognostic system. To accomplish this, predictive models are being developed using various approaches like physics-based and data-driven techniques. Physics-based models need huge computing power for simulations and domain knowledge for understanding and implementing the models. Alternatively, if we have substantial data for prediction, data-driven models can be used. In this research, we use data-driven approach for engine Remaining-Useful-Life (RUL) prediction on the NASA Commercial Modular …

Formula 1 Commentary Generator Using Generative Artificial Intelligence, Hiral Moliya

Master's Projects

This project aims to create a high-quality commentary generation system utilizing cutting-edge Generative AI technologies, with a particular focus on the T5 transformer-based text-to-text transfer transformer (T5). The primary goal is to create a fully autonomous and contextually aware commentary system that will be able to provide consistent and insightful commentary on dynamic events that reflect the level of detail normally associated with human commentary. Upon giving the input as a text input of the race events the model using large language models that are trained on a large range of datasets creates text-based commentary. The system in order to …

Parallel Powerplay: Optimizing Performance With Mapreduce And Kubernetes Fusion, Shradha Chaturvedi

Master's Projects

The combination of MapReduce (MR) & Kubernetes (K8s) strengths is not explored, and this study leverages the synergy between the two frameworks to meet the growing demands of data-intensive applications. First, this report elaborates on the existing literature work to understand the pros and cons of using MR and K8s, in what use cases these frameworks come to use, and investigates the effectiveness of research studies that explore the combination. This study aims to research the efficacy of the fusion of MR and K8s, considering these factors - application use case, infrastructure design, resource allocation, load balancing, and hypertuning parameters …

Advancing Phishing Protection: Employing Sophisticated Methods For Precise Url Evaluation, Abbhinav Reddie Nomuiia

Master's Projects

In short, the incidence of phishing - the illegal act of people pretending to be well-known companies to secure personal information - has skyrocketed in the past few years. In 2022 alone, 300,000 consumers in the United States were captured by scammers using phishing techniques, losing in all over $50 million. In the span of two weeks, over 510 million attempts occurred in a variety of sectors, particularly instant messaging platforms, package delivery businesses, and digital currency trading. Since most businesses have recognized that they are prone to these exposure cases, there has been a sixty percent increase in businesses …

Optimization Of Inter-Satellite Routing Using Lstm-Based Path Prediction Model, Yash Bhamare

Master's Projects

Satellite networks are one of the most important components that fulfill the world’s need for connectivity. To ensure that communication is efficient and reliable, robust routing algorithms are a must. Because, although it is true that certain routing characteristics may not be permanently and continuously flawless, a routing technique must effectively adapt to modifications in such network characteristics. The new routing method uses a Long Short-Term Memory (LSTM) model to manage dynamic metrics for Low Earth Orbit satellite networks. This LSTM model is aimed at predicting the optimal routing direction on the premise that a satellite is soon to be, …

Explaining The Maliciousness Of Urls Using Shap And Lime, Ayush Nair

Master's Projects

No system has ever reached the levels of proliferation that the Internet now enjoys. It stands as the most widely spread distributed system across the globe; yet this evolution has given rise to an ever-growing wave of malintent that challenges every user and entity on the vast expanse of cyberspace. Malicious URLs loom large as vulnerabilities leaving users naked as they traverse online landscapes, but cybersecurity experts craft models with esoteric algorithms in a bid to stem this tide and shield users from cybercrime. However, peering into the decision-making corridors of these models holds key importance, it’s through understanding such …

Multiclass Lung Disease Classification In Chest X-Ray Images: A Fine-Tuned Hybrid Cnn-Gnn Approach Using Transfer Learning And Feature Extraction, Vinay Vilas Khade

Master's Projects

In clinical practice, it is still difficult to accurately diagnose lung diseases from chest X-ray (CXR) images. In this study, we propose a new hybrid method for identifying several types of lung diseases using CXR images by combining Convolutional Neural Networks (CNNs) with Graph Neural Networks (GNNs). The framework of our proposed methodology takes advantage of CNN’s ability to extract detailed visual features and GNN’s capacity to understand complex relationships between these features, enabling comprehensive analysis in a multi-class classification setting of COVID-19, pneumonia, and normal lung conditions. We used multiple transfer learning models such as DenseNet201, VGG16, VGG19, MobileNetV2, …

Opinion Graphs Construction For Reviews Using Transfer Learning And Large Language Models, Yichen Lin

Master's Projects

With the rapid development of the Internet, reading online reviews before making a purchase, booking a hotel, or making a restaurant reservation has become a part of daily life. Customers often consider reviews as crucial supplementary information before making decisions on how to spend their money. However, reading many reviews to gain helpful information takes time and effort. This project proposes a new method OpinionGraphGenerator that aims to create opinion graphs from hotel reviews to reduce the high volume of text in reviews while preserving essential insights. In an opinion graph, vertices are semantically similar opinions, where each opinion consists …

Gesture Recognition Dynamics: Unveiling Video Patterns With Deep Learning, Nithish Reddy Agumamidi

Master's Projects

This paper, Gesture Recognition Dynamics: Revealing Video Patterns with Deep Learning, explores the combination of Long Short-Term Memory(LSTM) with Convolutional Neural Network(CNN) in the identification of convoluted human activities. The study assesses LSTM’s capability to capture temporal dependencies and CNN’s potential to apprehend and extract spatial characteristics to detect the gestures from UCF50. It further evaluates the architecture linkage of LSTM and CNN, which will improve the analytical capacity to interpret and validate dynamic gesture trends. The paper utilizes Mediapipe, an open-source framework created by Google specifically designed for extracting poses. The Mediapipe tool is well-designed to track important body …

Gender Classification Through Postural Analysis: A Comparative Study Of 2d Images And 3d Reconstructions., Prathamesh Dixit

Master's Projects

In computer vision, gender classification has become a vital task having applications in human-computer interaction, healthcare, and surveillance. In this study, we look at a two-step approach based on human joint information for gender classification. In this research, we use convolutional neural networks (CNNs).

With Leeds Sports Pose (LSP) dataset, we use a C5 pre-trained model to map and extract joint information from 2D RGB images and after pre-processing and background removal, we use PiFUHD to transform these 2D images into 3D representations. Next, we train our models on RGB images and joint images for both 2D and 3D representations. …

Understanding Distracted Driving And Gaze Patterns In A Driving Simulator: Simple Vs. Complex Challenges, Rishi Prabhat Narayana Sannala

Master's Projects

Distracted driving has grown in criticality over the recent years, given the numerous distractions that drivers face today, and which have further been magnified by the proliferation of in-vehicle technologies and mobile devices. Such distractions can seriously compromise a driver's ability to be fully focused on the road and to carry out timely responses and informed decisions that are key in minimizing the risks of a crash and maximizing road safety. The general aim of the research is to observe how simple versus complex distractors affect driving performance and gaze patterns. The eyetracker used is the Tobii Pro Fusion, synchronized …

An Empirical Analysis Of Adversarial Attacks In Federated Learning, Rohit Mapakshi

Master's Projects

In this paper, we experimentally analyze the susceptibility of selected Federated Learning (FL) systems to the presence of adversarial clients. We find that temporal attacks significantly affect model performance in FL, especially when the adversaries are active throughout and during the ending rounds of the FL process. Machine Learning models like Multinominal Logistic Regression, Support Vector Classifier (SVC), Neural Network models like Multilayer Perceptron (MLP), Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and tree-based machine learning models like Random Forest and XGBoost were considered. These results highlight the effectiveness of temporal attacks and the need …

Comparitive Analysis Of Time Series Forecasting Using Frequency Informed Dense Neural Networks And Lstm, Avinash Mangalore Suresh

Master's Projects

Time series forecasting influences our lives on a daily basis, being a versatile tool in various application areas like environmental studies, finance, medicine and much more. While there are many established statistical and deep learning approaches to model time series data, each implementation comes with their own set of drawbacks or areas of improvements. Most of the existing deep learning architectures and research have focused on modeling time series data in the time-domain exclusively. However training deep learning models in the time-domain has some drawbacks, mainly due to the inherent temporal dependence of each time-step on the time-steps before it, …

Personalizing Image Generation From Prompts Using Generative Ai, Mit Ramesh Jain

Master's Projects

The rapid advancements in Generative AI, particularly Text-to-Image (T2I) models, have opened up new possibilities for personalized image generation. Finetuning large T2I models for specific downstream tasks is a key approach to achieving tailored outputs. In recent years, Parameter-Efficient Fine-Tuning (PEFT) techniques have gained significant attention as a cost-effective and efficient solution for fine-tuning large models. Initially developed for fine-tuning large language models (LLMs), PEFT techniques have been extensively studied and compared in the context of language tasks. However, regarding the T2I domain, there is a lack of similarly exhaustive and detailed literature on PEFT. This research project, in the …

Load Balancing For Cloud-Based Applications, Nitish Ranjan

Master's Projects

Effective load balancing is critical in ensuring optimal resource utilization, reducing latency, and improving the overall performance of distributed systems. This report commences with a comprehensive literature review on existing load-balancing algorithms, examining their methodologies, strengths, and limitations within various computing environments, including cloud computing, data centers, and network traffic management. Despite significant advancements in this field, the dynamic nature of distributed systems, coupled with the ever-increasing demand for efficient data processing, poses ongoing challenges. In response, this study proposes a novel load-balancing algorithm to address these contemporary challenges. The approach leverages dynamic and hybrid load balancing, distinguishing it from …

Multimodal Techniques For Malware Classification, Jonathan Jiang

Master's Projects

The threat of malware has remained a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. This research adopted the structured nature of PE files incorporated with a multi-modal machinelearning approach, to classify malware types. Features extracted from the PE headers were used to train an LSTM model. Features extracted from the PE sections were used to train a CNN model. Probabilities produced from these two models were then concatenated and fed into an SVM classifier. This multi-modal approach demonstrated high accuracy by experimenting with and verifying the approach on a large and labeled dataset. …

An Attributed And Diverse Encoder-Decoder Processing Technique For Anomaly Detection., Kenneth Antony John

Master's Projects

Attributed graphs are graphs that contain extra information about the attributes of nodes and edges. They can be used to model a plethora of real-world scenarios like social networks, bank transactions, and even academic citation data. Anomalies in such graphs can be irregularities or unusual patterns that are observed in the attributes or the structure of the graph. Anomaly detection in attributed networks is a crucial task, aiming to identify such anomalies. Existing methodologies use various deep learning techniques using graph neural networks, graph encoder-decoder architectures, and multi-layer perceptions. This study proposes a new approach to improve the existing methods …

Explainable Reinforcement Learning For Network Routing Optimization, Yu Xiu

Master's Projects

Software-defined Networking (SDN) provides a solution for configuring multiple network devices by offering a centralized controller architecture. Network routing is one of the most crucial problems in network configuration. In particular, the surging network traffic demands require efficient routing techniques to load balance communication links. In order to optimize the communication path’s utilization and reduce request blocking, this project utilizes Reinforcement Learning (RL) to decide the routes for given network requests. Furthermore, we adopt Explainable Reinforcement Learning (XRL) to explain the RL learning agent’s decision-making process to enhance the trustworthiness of our approach. In particular, we focus on Feature Importance …

Reinforcement Learning And Hidden Markov Models For Simulating And Analyzing Social Engineering Attacks., Bharkavi Sachithanandam

Master's Projects

The advent of the internet has revolutionized communication and connectivity on a global scale. Now every computer is connected to the internet. Although this technological advancement has made human life easier, this has also led to an increase in sophisticated methods of exploitation. Social Engineering is such a prominent threat to the human community. Social engineering attackers manipulate the victim into giving away sensitive details. Understanding the dynamics of social engineering is crucial for developing measures to help individuals and organizations avoid falling prey to these deceptive tactics. Hence it is essential to understand the attackers. Thus gaining insight into …

Optimizing Web Design Code Generation: A Comparative Study Of Finetuning, Pretrained Models, And Rag (Retrieval Augmented Generation), Srinivas Rao Chavan

Master's Projects

Website Creation is revolutionized by automated code generation, reducing the development effort, speeding the production process, and ensuring consistency in design. Automated web design code generation has emerged as a transformative tool bridging the gap between design and development. In this research, a website design tool is developed and used to create visual layouts, exporting them as JSON designs. These JSON outputs were then transformed into textual prompts, optimized using established HCI principles and UI/UX rules to ensure consistency, visual hierarchy, aesthetics and minimalistic design, accessibility, user-friendly navigation and flexibility. These generated prompts were fed into large language models for …

Predicting Quality Of Life In Driving Scene Using Image Recognition Techniques And User Group Information, Ployrada Suvarnakuta

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study presents a machine learning approach for predicting perceived urban Quality of Life (QoL) by integrating visual features from street-level imagery with personal attributes, including demographic, socioeconomic, and travel behavior data. Using datasets from Bangkok and London, we trained supervised models—Support Vector Machines and Multilayer Perceptrons—under multiple input configurations to evaluate the contribution of each data type. Results show that combining visual and personal features improves prediction accuracy compared to using visual features alone. Statistical feature selection identified income, education, housing stability, and travel patterns as consistently important predictors, with some variation across urban contexts. These findings underscore the …

Outdoor Navigation Support Using Machine Learning For People With Visual Impairment, Fatmaelzahraa Eltaher

Doctoral

Navigating urban environments is challenging for People with Visual Impairments (PVI). Many PVI opt to use navigation systems, such as Google Maps. However, current navigation systems often lack information crucial for PVI, such as the presence of traffic lights or roadworks. Knowing these features would enable PVI to understand their surroundings better and choose routes that suit their preferences. For instance, 79.6\% of PVI surveyed in this thesis prefer road junctions (termed intersections) controlled by traffic lights.

This research contributes to advancing navigation support for PVI in urban environments by focusing on annotating maps with useful environmental information. It proposes …

Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey

CCAC Theses and Dissertations

Cybersecurity frameworks developed by a variety of organizations and implemented by a much larger collection of organizations differ in their focus and application. Whether designed by a private or government organization, the primary goal is to provide a framework to assess and reduce risk. The Department of Defense (DoD) has recently implemented the second version of the Cybersecurity Maturity Model Certification (CMMC 2.0). In some situations, compliance with CMMC 2.0 has already become mandatory for the Defense Industrial Base (DIB). Compliance will soon be required for all Large Businesses (LB) and Small Businesses (SB) within the DIB. While COBIT 19 …

Combating Disinformation On Social Media Networks With Media And Information Literacy Training For Social Media Network Users, Oscar Kwok Chao Ho

CCAC Theses and Dissertations

In the Internet age, social media networks (SMNs), such as Facebook (FB), Instagram (IG), and Twitter (TW), have gained popularity and become an essential part of human life. SMNs provide ease of connection to family, friends, and communities; however, they increase the chances social media network users (SMN users) will disclose private information (PI), causing critical harm to SMN users’ information privacy (IP). Furthermore, SMN users are exposed to significant amounts of disinformation, misinformation, or fake news, which they share without realizing the information is untrustworthy.

The goal of this developmental research was to investigate, examine, and understand the effects …

A Technique For Visualization Of Multivariate Categorical Data, Janice James

CCAC Theses and Dissertations

Multivariate Categorical Data (MCD) plays a significant role in many industries, and the ability to understand the data is critical for insight and decision making. Visualization is a key tool for understanding the data. This dissertation designed and implemented a novel technique for visualizing MCD called Pivoting Parallel Charts (PPC). The design of PPC was informed by studying several existing MCD visualization techniques.

PPC visualizes MCD as a sequence of parallel axes with affixed bar charts. A user-specified axis, called the pivot, acts as the crucial point of consideration for all data relationships. The bar charts are color-coded by the …

Algorithms For Coordinating Multiple Autonomous Vehicles Under Various Constraints With Emphasis On Workload Balancing, Abhishek Patil

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on developing algorithms to solve the problem of coordinating multiple autonomous vehicles under various constraints, aiming to produce practical solutions for real-world applications. Built upon three journal publications addressing two coordination-related problems in different domains, this research document tackles the challenges of heterogeneity constraints and cable entanglement issues encountered by autonomous vehicle systems.

The first problem tackles task allocation and path planning for heterogeneous ground mobile vehicles operating in a 2D environment with asymmetric travel costs. By enhancing previous Primal-Dual approximation heuristic methods, novel techniques are introduced to manipulate dual variables and achieve balanced workload distribution, ultimately …

Specification And Implementation Of Arm Isa Using Adl, Jonathan A. Rabideau

Dissertations, Master's Theses and Master's Reports

In this project, we implement a specification of ARM ISA using the ADL system. Current progress includes 79% of planned instructions, 79% of which are complete for our purposes, and also includes components such as a proxy kernel and a temporary preprocessor. Simple programs compiled directly from gcc can already be properly assembled and simulated using the tools generated by ADL. Once the specification is complete, we can integrate it with other components for testing ideas and theories. This report covers our progress and future plans regarding this project, including some background material and problem solutions.

Enhancing Iot Security: Optimizing Anomaly Detection Through Machine Learning, Maria Balega, Waleed Farag, Xin-Wen Wu, Soundarararjan Ezekiel, Zaryn Good

Computer Science Articles

As the Internet of Things (IoT) continues to evolve, securing IoT networks and devices remains a continuing challenge. Anomaly detection is a crucial procedure in protecting the IoT. A promising way to perform anomaly detection in the IoT is through the use of machine learning (ML) algorithms. There is a lack of studies in the literature identifying optimal (with regard to both effectiveness and efficiency) anomaly detection models for the IoT. To fill the gap, this work thoroughly investigated the effectiveness and efficiency of IoT anomaly detection enabled by several representative machine learning models, namely Extreme Gradient Boosting (XGBoost), Support …

A Robust Form Understanding System Using Graph-Based Neural Network, Chavin Chuangchaichatchavarn

Chulalongkorn University Theses and Dissertations (Chula ETD)

In this work, we address the challenge of form understanding in real-world documents affected by OCR noise and layout uncertainty. We introduce TONDFU, a bilingual Thai and English dataset consisting of official documents such as vehicle registrations and utility bills, annotated for entity labelling and entity linking. We also introduce a noisy character feature extractor that captures lexical and spatial patterns to improve the model's robustness against noisy textual content. This feature is integrated with geometric, visual, and semantic features in the graph-based model. Experiments show that the noisy character feature outperforms the frequency histogram baseline, and with pretraining on …